Robust Oscillation Detection Index and Characterization of Oscillating Signals for Valve Stiction Detection

Abstract

Detection of oscillation is a necessary step for determining valve stiction, which is a common problem in process industries. More specifically, many stiction detection methods assume persistency of the oscillation pattern in the control loop signals, which is a necessary condition to achieve reliable detection results. However, the existing oscillation detection methods do not study the persistency of the oscillation patterns directly. Instead, these methods try to discover the presence of specific periodic characteristics in the signals, their autocorrelation, or power-spectrum. This paper aims to propose an oscillation detection method that directly evaluates the similarity of the shapes of subsequent oscillation periods by means of correlation coefficient. The proposed oscillation detection method is compared against five other methods reported in the literature. Furthermore, the stiction detection methods assuming oscillation in control loops have different robustness to the disturbances corrupting the analyzed signals. In order to prepare a basis for selecting the right stiction detection methods for the available data automatically, the paper introduces two indexes quantifying mean-nonstationarity and presence of noise in oscillating signals.

Oscillation Detection in Process Industries by a Machine Learning-Based Approach

“…If both values are kept close to constant over time, oscillation is detected. Zakharov and co-workers , designed an algorithm that evaluates the correlation between two periods of oscillation. If the correlation is high, oscillation is detected.…”

Modeling, Detection and Quantification, and Compensation of Stiction in Control Loops: The State of the Art

“…This assumption may reduce the stiction detection robustness in the presence of system disturbances. Zakharov and Jamsa-Jounela 108 proposed an oscillation detection method that evaluates the similarity of the oscillation periods by means of a correlation coefficient and compared it against five other methods reported in the literature. The work also introduced two indexes to quantify the mean-nonstationarity and the presence of noise in oscillating signals.…”

Time-Frequency Analysis of Plant-Wide Oscillations Using Multivariate Intrinsic Time-Scale Decomposition

“…2 The presence of oscillatory variables in a process plant may result in inferior products, larger rejection rates, increased consumption of energy, and even compromising process stability. 3 Increasing emphasis on process safety and plant profitability strongly motivates the research for techniques to automatically detect plant-wide oscillations before resolving the control problem via proper service and troubleshooting. 4 In the past two decades, a large amount of techniques for plant-wide oscillations characterization are invented, where most of available techniques are designed for being applied loop-by-loop.…”

“…An oscillation is usually generated in one closed loop, then it propagates through the interconnected loops and causes plant-wide oscillations in the control system . The presence of oscillatory variables in a process plant may result in inferior products, larger rejection rates, increased consumption of energy, and even compromising process stability . Increasing emphasis on process safety and plant profitability strongly motivates the research for techniques to automatically detect plant-wide oscillations before resolving the control problem via proper service and troubleshooting …”